CN108318823A - A kind of lithium battery charge state evaluation method based on noise tracking - Google Patents
A kind of lithium battery charge state evaluation method based on noise tracking Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The present invention relates to a kind of lithium battery charge state evaluation methods based on noise tracking, include the following steps：1) off-line model is built, and the off-line model includes open-circuit voltage model and equivalent-circuit model；2) On-line Estimation establishes SOC On-line Estimation models using the off-line model, and SOC estimations are realized based on noise tracking.The On-line Estimation specifically includes following steps：201) it is based on current integration formula and the off-line model establishes non-linear state space equation；202) moving horizon estimation strategy is combined, augmentation non-linear state space equation and SOC On-line Estimation models are established；203) according to detection voltage and current, estimated using the estimation of SOC On-line Estimation model realization process noises, measurement noise estimation and SOC.Compared with prior art, the present invention has can reduce the current measurement errors in Current integrating method by tracking process noise, have lithium battery SOC estimation accuracies and high reliability.
Description
Technical field
The present invention relates to battery management systems, are estimated more particularly, to a kind of lithium battery charge state based on noise tracking
Method.
Background technology
Because having many advantages, such as that energy density is big, output power is high, charge discharge life is long, lithium ion battery is widely used to
The emerging technology areas such as mancarried electronic aid, electric vehicle, family's energy storage and space technology.Battery charge state (State of
Charge, i.e. SOC) Core Feature one of of the estimation as lithium battery management system, for improving battery utilization rate, extending battery
Service life, raising battery safety in utilization improve most important.Current battery manages in system SOC estimations and mainly uses electric current
Integration method.This method SOC estimated accuracies are primarily limited to two aspect of initial SOC evaluated errors and current measurement errors.Due to electricity
It flows integration method and lacks the feedback mechanism for eliminating initial error, and current measurement noise can not be carried out to track and correct in time, because
This precision is relatively low, can not fully meet actual demand.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be tracked based on noise
Lithium battery charge state evaluation method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of lithium battery charge state evaluation method based on noise tracking, includes the following steps：
1) off-line model is built, and the off-line model includes open-circuit voltage model and equivalent-circuit model；
2) On-line Estimation establishes SOC On-line Estimation models using current integration formula and the off-line model, is based on noise
SOC estimations are realized in tracking.
The open-circuit voltage model is the functional relation of open-circuit voltage and SOC, is embodied as：
Wherein, VOCFor open-circuit voltage, SOC is battery charge state, and j is jth rank in polynomial function, β1jFor multinomial
Coefficient, M are the total exponent number of multinomial, and subscript k is sampling instant.
The equivalent-circuit model is the functional relation of circuit parameter and SOC, and the circuit parameter includes open-circuit voltage, Europe
The resistance and capacitance of nurse internal resistance and single order or multistage RC network.
The equivalent-circuit model is the equivalent-circuit model of single order or multistage RC network.
In the equivalent-circuit model of the single order or multistage RC network, ohmic internal resistance, the resistance of RC network and capacitance and SOC
Functional relation be：
Wherein, n is RC network exponent number, R0For ohmic internal resistance, RnFor the polarization resistance on n-th order RC network, CnFor n-th order
Equivalent capacity on RC network, SOC are battery charge state, and j is jth rank in polynomial function, β2j、β2n+1,jAnd β2n+2,jIt is more
Binomial coefficient, M are the total exponent number of multinomial, and subscript k is sampling instant.
The On-line Estimation specifically includes following steps：
201) it is based on current integration formula and the off-line model establishes non-linear state space equation；
202) moving horizon estimation strategy is combined, establish augmentation non-linear state space equation and is tracked based on noise
SOC On-line Estimation models；
203) it according to detection voltage and current, using SOC On-line Estimations model realization process noise estimation, measures and makes an uproar
Sound estimates and SOC estimations.
The non-linear state space equation includes：
State equation：xk+1=F (xk,uk)+wk
Observational equation：yk=h (xk,uk)+vk
Wherein, state vector x=[SOC, V1,…,Vn]T, input variable u=I, observational variable y=Vb, VbFor battery electricity
Pressure, w and v are respectively process noise and measurement noise, and the two is mutual indepedent and is white Gaussian noise, and covariance is respectively Qw
And R.I is load current, and Δ t is sampling period, VOCFor open-circuit voltage, C is battery capacity, and SOC is battery charge state, and n is
RC network exponent number, R0For ohmic internal resistance, RnFor the polarization resistance on n-th order RC network, CnFor the equivalent electricity on n-th order RC network
Hold, VnFor the voltage on n-th order RC network, τn=RnCnFor the time constant of n-th order RC network, subscript k is sampling instant.
In step 202), using process noise as state variable, augmentation non-linear state space equation, and the increasing are established
F (x in the state equation of wide non-linear state space equationk,uk) replace with F (zk,uk),
Wherein, z=[SOC, V1,…,Vn,w0,…,wn]TFor augmented state vector, process noise be accordingly converted into γ=
[w,θ]T, with measurement noise independently of each other and be white Gaussian noise, covariance Q.
The SOC On-line Estimations model is expressed as：
Wherein,For arrival cost, Q is process noise covariance, and R is measurement noise covariance, and L is rolling time horizon window
Length, T are current time, and cost function usesApproximate substitution, P assist for evaluated error
Variance.
Step 203) specifically includes：
231) it initializes；
232) the SOC On-line Estimations model is solved at the T moment, obtains current state estimated value, process noise
Estimation and measurement noise estimated value；
233) SOC for obtaining the T moment is calculated according to state equation；
234) varivance matrix is updated；
235) T=T+1 is enabled, new measurement data set y is constructedT, return to step 232).
The more new formula of evaluated error side's covariance P is：
Wherein,
Compared with prior art, the invention has the advantages that：
1, the method for the present invention can reduce the current measurement errors in Current integrating method by tracking process noise, pass through tracking
Capability for correcting of the back voltage to SOC estimation can be improved in measurement noise estimation, to ensure under industrial detection environment, lithium battery
The accuracy and reliability of SOC estimation, it is final to promote battery management system overall performance.
2, the off-line model that the present invention establishes includes open-circuit voltage model and equivalent-circuit model, and accuracy is high, is online
Estimation provides basis.
Description of the drawings
Fig. 1 is the principle schematic of the method for the present invention；
Fig. 2 is the structure that SOC estimates device in embodiment of the present invention；
Fig. 3 is current excitation and voltage responsive oscillogram in embodiment of the present invention；
Fig. 4 is lithium battery equivalent-circuit model figure in embodiment of the present invention.
Specific implementation mode
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
As shown in Figure 1, the present invention provides a kind of lithium battery charge state evaluation method tracked based on noise, including it is following
Step：1) off-line model is built, and the off-line model includes open-circuit voltage model and equivalent-circuit model；2) On-line Estimation, profit
SOC On-line Estimation models are established with the off-line model, SOC estimations are realized based on noise tracking.On-line Estimation process is specially：
201) it is based on current integration formula and the off-line model establishes non-linear state space equation；202) moving horizon estimation is combined
Strategy, the SOC On-line Estimation models established augmentation non-linear state space equation and tracked based on noise；203) according to detection electricity
Pressure and electric current are estimated using SOC On-line Estimations model realization process noise estimation, measurement noise estimation and SOC.This method
The current measurement errors in Current integrating method can be reduced by tracking process noise, be can be improved back by the estimation of tracking measurement noise
Feedthrough voltage is to the capability for correcting of SOC estimation, to ensure under industrial detection environment, the accuracy of lithium battery SOC estimation and
Reliability, it is final to promote battery management system overall performance.
The above method can be applied to lithium battery management system, carry out the state-of-charge estimation of lithium battery energy storage battery equipment.At this
In invention specific implementation mode, the structure of lithium battery SOC estimation devices is as shown in Fig. 2, including microcontroller 100, memory
102, electric current and voltmeter 104, SOC estimators 106.Microcontroller 100 generally controls SOC estimation devices, electric current and voltage
Table.Memory 102 is for program performed by storage control.Electric current and voltmeter 104 are measured according to the control of controller 100
Electric current and voltage.SOC estimators 106 provide estimation knot according to the controlled estimation SOC of controller 100, and to controller 100
Fruit.The foundation of SOC estimators includes off-line model structure and on-line Algorithm application.
The detailed process of the above-mentioned lithium battery charge state evaluation method based on noise tracking is as follows：
Step S11 carries out open-circuit voltage experiment to the battery, establishes open-circuit voltage model.The battery is first with constant current
Constant voltage mode charges to blanking voltage, and stands certain time；Again with 1C multiplying power current versus cell continuous discharges to specific SOC
Afterwards, 1 hour is stood.In whole process, battery terminal voltage and load current are acquired with 1Hz sampling frequency synchronizations.According to each quiet
SOC a little and its corresponding open-circuit voltage measured value are set, open-circuit voltage and the functional relation of SOC are established.The present invention is specifically real
Apply the middle functional relation that open-circuit voltage and SOC are indicated using 10 rank multinomial forms:
In formula, the parameter of required identification is multinomial coefficient β1i, required SOC calculates according to Current integrating method：
In formula, SOC (0) is battery initial SOC value, and C is battery capacity, and I is load current.Marriage relation formula (1) and
(2), using least square method to β1jParameter identification is carried out, determines open-circuit voltage and the functional relation of SOC.
Step S12 carries out charge-discharge characteristic test to the battery, and establishes equivalent-circuit model based on institute's measured data.
The battery first charges to blanking voltage in a manner of constant current constant voltage, and stands certain time；Specific fill is implemented to the battery again
Discharge test operating mode.In whole process, battery terminal voltage and load current are acquired with 1Hz sampling frequency synchronizations.The present invention has
Body uses HPPC standard testings operating mode as charge-discharge test operating mode in implementing, as shown in Figure 3.Equivalent-circuit model includes three
Point：Open-circuit voltage VOC, ohmic internal resistance R0And single order or multistage RC network, wherein RC network is by polarization resistance and equivalent capacity group
At open-circuit voltage VOCIt is determined by step S11.
Single order equivalent-circuit model is used in present invention specific implementation, as shown in figure 4, wherein VbFor cell voltage, I is negative
Carry electric current.The equivalent-circuit model meets following voltage-current relationship：
Vb=VOC-V1-IR0(4)
For in a sampling period Δ t, the discrete form expression of relational expression (3) and (4) can must be expressed as：
Vb,k=VOC(SOCk)-V1,k-IkR0,k (6)
Wherein, timeconstantτ1=R1C1.In the present invention is embodied, Δ t is 1s.
In the present invention is embodied, circuit parameter R in formula (5) and (6)0、R1And C16 ranks are used with the functional relation of SOC
Polynomial form indicates：
Wherein, required identified parameters are multinomial coefficient β2j、β3jAnd β4j.In identification process, it is based on formula (5) and formula
(6), the voltage response curves in Fig. 2 are fitted using least square method, obtain β2j、β3jAnd β4j.So far, off-line model
Structure is completed.
Step S21 establishes non-linear state space equation based on current integration formula and off-line model.The state space side
Journey is represented by：
State equation：xk+1=F (xk,uk)+wk (10)
Observational equation：yk=h (xk,uk)+vk (11)
Meet constraints：
xk∈ X, wk∈ W, vk∈V (12)
xk∈[xL, xU] (13)
In the present invention, definition status vector is xk=[SOCk,V1,k]T, input variable uk=Ik, observational variable yk
=Vb,k。wkAnd vkProcess noise and measurement noise are indicated respectively, independently of each other and are white Gaussian noise, the two covariance difference
For QwkAnd Rk.Nonlinear function F (x in state equation and observational equationk,uk) and h (xk,uk) be respectively：
h(xk,uk)=VOC(SOCk)-V1,k-IkR0,k (15)
In formula, VOC, R0、R1And C1Functional relation with SOC is obtained by off-line model.
Step S22 implements noise tracking and state estimation to be synchronous, converts process noise to state variable, i.e.,：wk+1
=wk+θk, θkFor covariance QθkWhite Gaussian noise, establish augmentation non-linear state space equation.Define z=[SOC, V1,w0,
w1]TFor augmented state vector, formula (10) is accordingly converted into：
zk+1=F (zk,uk)+γk (16)
Wherein, process noise γk=[wk,θk]T, independently of each other and be white Gaussian noise with measurement noise, covariance is
Qk。
Wherein, z=[SOC, V1,w0,w1]TFor augmented state vector.
In conjunction with non-linear state space equation and moving horizon estimation strategy, SOC On-line Estimation models are established, each calculation is adjusted
Method parameter.Assuming that system initial state is z0, for the k moment, all measurement data areInterference sequence isAnd z0Priori estimates meet mean value and beCovariance is P0Normal distribution.If fixed data time domain is L,
So at the T moment, state estimation problem can be equivalent to following rolling time horizon optimization problem：
Meet constraints (7)-(9), (11), (15)-(17)
0≤SOCk≤1 (19)
In above-mentioned model,For arrival cost, useApproximate substitution.Parameter R
For process noise covariance, reflect current measurement errors and off-line model error in current integration process.Parameter Q is to measure to make an uproar
Sound covariance, reflecting voltage measurement error.Parameter P is evaluated error covariance, reflects the confidence to initial estimation.Pass through solution
Current time SOC estimation, process noise estimated value and measurement noise estimated value can be obtained in the problem.Of the invention specific real
Shi Zhong, L Synthesize estimation precision and actuarial time carry out preferred.Q, R are adjusted according to each state variable order of magnitude.
P is updated using following formula in formula：
Items are defined as follows in formula：
So far, optimization aim (16) is represented by：
Step S23 realizes process noise estimation, measurement noise according to detection voltage and current using On-line Estimation model
Estimation and SOC estimations.When being estimated using non-linear rolling time horizon method, including following five steps：
1, it initializes：Given P0, Q, R, initial estimated stateWith rolling time horizon length of window L；
2, at the T moment, solving-optimizing problem (18) obtains current state estimated valueProcess noise is estimatedWith measurement noise estimated value
3, according to formula (10), state estimation is utilizedWith process noise estimated valueWhen obtaining current T
The state SOC at quarter；
4, subsequent time varivance matrix P is calculated according to formula (20)T-L；
5, at the T+1 moment, y is measuredT, construct new measurement data set, return to step 2.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be in the protection domain being defined in the patent claims.
Claims (10)
1. a kind of lithium battery charge state evaluation method based on noise tracking, which is characterized in that include the following steps：
1) off-line model is built, and the off-line model includes open-circuit voltage model and equivalent-circuit model；
2) On-line Estimation establishes SOC On-line Estimation models using the off-line model, and SOC estimations are realized based on noise tracking.
2. the lithium battery charge state evaluation method according to claim 1 based on noise tracking, which is characterized in that described
Open-circuit voltage model is the functional relation of open-circuit voltage and SOC, is embodied as：
Wherein, VOCFor open-circuit voltage, SOC is battery charge state, and j is jth rank in polynomial function, β1jFor multinomial coefficient, M
For the total exponent number of multinomial, subscript k is sampling instant.
3. the lithium battery charge state evaluation method according to claim 1 based on noise tracking, which is characterized in that described
Equivalent-circuit model is the functional relation of circuit parameter and SOC, and the circuit parameter includes open-circuit voltage, ohmic internal resistance and one
The resistance and capacitance of rank or multistage RC network.
4. the lithium battery charge state evaluation method according to claim 3 based on noise tracking, which is characterized in that described
Equivalent-circuit model is the equivalent-circuit model of single order or multistage RC network, wherein ohmic internal resistance, RC network resistance and capacitance
Functional relation with SOC is：
Wherein, n is RC network exponent number, R0For ohmic internal resistance, RnFor the polarization resistance on n-th order RC network, CnFor n-th order RC nets
Equivalent capacity on network, SOC are battery charge state, and j is jth rank in polynomial function, β2j、β2n+1,jAnd β2n+2,jFor multinomial
Coefficient, M are the total exponent number of multinomial, and subscript k is sampling instant.
5. the lithium battery charge state evaluation method according to claim 4 based on noise tracking, which is characterized in that described
On-line Estimation specifically includes following steps：
201) it is based on current integration formula and the off-line model establishes non-linear state space equation；
202) moving horizon estimation strategy, the SOC for establishing augmentation non-linear state space equation and being tracked based on noise is combined to exist
Line estimates model；
203) according to detection voltage and current, estimated using SOC On-line Estimations model realization process noise estimation, measurement noise
Meter and SOC estimations.
6. the lithium battery charge state evaluation method according to claim 5 based on noise tracking, which is characterized in that described
Non-linear state space equation includes：
State equation：xk+1=F (xk,uk)+wk
Observational equation：yk=h (xk,uk)+vk
Wherein, state vector x=[SOC, V1,…,Vn]T, input variable u=I, observational variable y=Vb, VbFor cell voltage；W and
V is respectively process noise and measurement noise, and the two is mutual indepedent and is white Gaussian noise, and covariance is respectively QwAnd R；I
For load current, Δ t is sampling period, VOCFor open-circuit voltage, C is battery capacity, and SOC is battery charge state, and n is RC network
Exponent number, R0For ohmic internal resistance, RnFor the polarization resistance on n-th order RC network, CnFor the equivalent capacity on n-th order RC network, VnFor
Voltage on n-th order RC network, τn=RnCnFor the time constant of n-th order RC network, subscript k is sampling instant.
7. the lithium battery charge state evaluation method according to claim 6 based on noise tracking, which is characterized in that step
202) in, augmentation non-linear state space equation, and the augmentation nonlinear state are established using process noise as state variable
F (x in the state equation of space equationk,uk) replace with F (zk,uk),
Wherein, z=[SOC, V1,…,Vn,w0,…,wn]TFor augmented state vector, process noise is accordingly converted into γ=[w, θ
]T, with measurement noise independently of each other and be white Gaussian noise, covariance Q.
8. the lithium battery charge state evaluation method according to claim 7 based on noise tracking, which is characterized in that described
SOC On-line Estimation models are expressed as：
Wherein,For arrival cost, Q is process noise covariance, and R is measurement noise covariance, and L is rolling time horizon length of window,
T is current time, and cost function usesApproximate substitution, P are evaluated error association side
Difference.
9. the lithium battery charge state evaluation method according to claim 8 based on noise tracking, which is characterized in that step
203) it specifically includes：
231) it initializes；
232) the SOC On-line Estimations model is solved at the T moment, obtains current state estimated value, process noise estimation
With measurement noise estimated value；
233) SOC and noise for obtaining the T moment are calculated according to state equation；
234) evaluated error covariance is updated；
235) T=T+1 is enabled, new measurement data set y is constructedT, return to step 232).
10. the lithium battery charge state evaluation method according to claim 9 based on noise tracking, which is characterized in that institute
The more new formula for stating evaluated error covariance P is：
Pk+1=BkQkBk′+Ak(Pk-PkC′(R+CkPkCk′)-1CkPk)A′
Wherein,
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| CN112379270A (en) * | 2020-11-13 | 2021-02-19 | 哈尔滨工业大学 | Electric vehicle power battery state of charge rolling time domain estimation method |
| CN114252797A (en) * | 2021-12-17 | 2022-03-29 | 华中科技大学 | Uncertainty estimation-based lithium battery remaining service life prediction method |
| CN114252797B (en) * | 2021-12-17 | 2023-03-10 | 华中科技大学 | Uncertainty estimation-based lithium battery remaining service life prediction method |
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